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OFA is a unified sequence-to-sequence pretrained model (support English and Chinese) that unifies modalities (i.e., cross-modality, vision, language) and tasks (finetuning and prompt tuning are supported): image captioning (1st at the MSCOCO Leaderboard), VQA (link), visual grounding, text-to-image generation, text classification, text generation, image classification, etc. We provide step-by-step instructions for pretraining and finetuning and corresponding checkpoints (check official ckpt [EN|CN] or Hugging Face ckpt).

We sincerely welcome contributions to our project. Feel free to contact us or send us issues / PRs!

Online Demos

We provide online demo via Hugging Face Spaces for you to interact with our pretrained and finetuned models. Below are the links to the demos:

• Image Captioning [ModelScope | Spaces]
• Visual Grounding [ModelScope | Spaces]
• Visual Question Answering [ModelScope | Spaces]
• Text-to-Image Generation [ModelScope | Spaces]
• Generic Interface [Spaces]
• Chinese OCR [ModelScope | Spaces]

Also we provide Colab notebooks for you to better perceive the procedures. Click here to check them out!

Use in Hugging Face Transformers

We support the inference of OFA in Hugging Face Transformers. Check the README and Colab Notebook for more information. Codes are released in this branch https://github.com/OFA-Sys/OFA/tree/feature/add_transformers

News

• 2023.5.11: Two papers (OFA-OCR and OFA-prompt) are accepted by ACL. The evaluation scripts and checkpoints of OFA-OCR are released.
• 2023.1.11: Released MuE (https://arxiv.org/abs/2211.11152), which significantly accelerates OFA with little performance degradation. Many thanks to the first author, Shengkun Tang (@Tangshengku). See the branch feature/MuE and PR for more information.
• 2022.12.20: Released OFA-OCR, a model for Chinese text recognition based on OFA. Check our paper and demo.
• 2022.12.7: Released the MMSpeech an ASR pre-training method based on OFA. Check our paper here! Please see the README_mmspeech.md for further details.
• 2022.8.16: Released the Chinese version of OFA. OFA-CN needs only switching to bpe_dir=../../utils/BERT_CN_dict and bpe=bert and using our provided Chinese checkpoints in checkpoints_cn.md. Temporarily, we only provide base-size and large-size pretrained checkpoints and finetuned checkpoints on MUGE Caption and the Chinese version of RefCOCO(-/+/g) (to release soon).
• 2022.8.5: Released support of prompt tuning for OFA. Check our paper here! Please see the prompt_tuning.md for further details.
• 2022.7.7: Updated support of OFA on Hugging Face transformers (fixed bugs in forward, add sequence generator from Fairseq to ensure performance, etc.). Refer to the doc transformers.md and the branch feature/add_transformers.
• 2022.6.17: Released the pretrained checkpoint of OFA-Huge. To use it, set --arch=ofa_huge in the script.
• 2022.5.15: OFA was accepted by ICML 2022

More News

• 2022.4.28: Add support of inference on **Hugging Face transformers**. For how to use it, please refer to the doc [transformers.md](transformers.md) and our [Hugging Face models](https://huggingface.co/OFA-Sys).
• 2022.4.16: Released lightweight pretrained models **OFA-Medium** (~93M params) and **OFA-Tiny** (~33M params) in [checkpoints.md](checkpoints.md). To use them, you just need to load the corresponding checkpoint and set `--arch=ofa_medium` or `--arch=ofa_tiny` in the scripts.
• 2022.3.23: Added [Encouraging Loss](https://arxiv.org/pdf/2110.06537.pdf) as a feature. See [README_EncouragingLoss.md](README_EncouragingLoss.md). Leveraging this feature, OFA-Large has achieved improved results in both VQA (**test-std acc: 80.67**) and Image Classification (**test acc: 85.6**) recently.
• 2022.3.21: Released codes for pretraining OFA.
• 2022.3.18: Released the finetuned OFA-Base (~180M parameters) checkpoints and running scripts for vision & language tasks, including: Caption (146.4 CIDEr), VQA (78.07 on test-std), SNLI-VE (89.3 on dev), RefCOCO (90.67 on testA), RefCOCO+ (87.15 on testA) and RefCOCOg (82.31 on test-u).
• 2022.3.11: Released the finetuning & inference code/checkpoints for Gigaword.
• 2022.3.08: Released the pretrained checkpoint of OFA-Base in checkpoints.md. To use OFA-Base, you just need to load ofa_base.pt and change --arch=ofa_large to --arch=ofa_base in the training scripts.
• 2022.3.07: Released the finetuning & inference code/checkpoints for Image Classification, which achieves 85.0 accuracy on ImageNet-1K, slightly better than reported in OFA paper.
• 2022.3.04: Released the finetuning & inference code/checkpoints for Text-to-Image Generation.
• 2022.3.03: Released the finetuning & inference code/checkpoints for SNLI-VE and GLUE.
• 2022.2.22: Released the finetuning & inference code/checkpoints for Visual Question Answering, which can reproduce the reported VQA accuracy in OFA paper (80.02 on test-std). Check our results on the VQA Challenge.
• 2022.2.15: Released finetuning & inference code/checkpoints for Referring Expression Comprehension
• 2022.2.10: Released the inference code & finetuned checkpoint for Image captioning, which can reproduce the results on COCO Karparthy test split (149.6 CIDEr). OFA also achieves No.1 on the COCO image captioning online leaderboard Link (marked as M6-Team).

Model Card

We list the parameters and pretrained checkpoints of OFAs below. For finetuned checkpoints, please refer to checkpoints.md.

Model	Ckpt	Params	Backbone	Hidden size	Intermediate size	Num. of heads	Enc layers	Dec layers
OFATiny	Download	33M	ResNet50	256	1024	4	4	4
OFAMedium	Download	93M	ResNet101	512	2048	8	4	4
OFABase	Download	180M	ResNet101	768	3072	12	6	6
OFALarge	Download	470M	ResNet152	1024	4096	16	12	12
OFAHuge	Download	930M	ResNet152	1280	5120	16	24	12

Results

Below we demonstrate the results of OFAs on cross-modal understanding and generation.

Task	Image Captioning	VQA	Visual Entailment	Referring Expression Comprehension
Dataset	COCO	VQA v2	SNLI-VE	RefCOCO	RefCOCO+	RefCOCOg
Split	Karpathy test (CE/CIDEr)	test-dev/test-std	val/test	val/test-a/test-b	val/test-a/test-b	val-u/test-u
Metric	CIDEr	Acc.	Acc.	Acc.
OFATiny	119.0 / 128.7	70.3 / 70.4	85.3 / 85.2	80.20 / 84.07 / 75.00	68.22 / 75.13 / 57.66	72.02 / 69.74
OFAMedium	130.4 / 140.3	75.4 / 75.5	86.6 / 87.0	85.34 / 87.68 / 77.92	76.09 / 83.04 / 66.25	78.76 / 78.58
OFABase	138.2 / 146.7	78.0 / 78.1	89.3 / 89.2	88.48 / 90.67 / 83.30	81.39 / 87.15 / 74.29	82.29 / 82.31
OFALarge	142.2 / 150.7	80.4 / 80.7	90.3 / 90.2	90.05 / 92.93 / 85.26	85.80 / 89.87 / 79.22	85.89 / 86.55
OFAHuge	145.3 / 154.9	82.0 / 82.0	91.0 / 91.2	92.04 / 94.03 / 88.44	87.86 / 91.70 / 80.71	88.07 / 88.78

Requirements

• python 3.7.4
• pytorch 1.8.1
• torchvision 0.9.1
• JAVA 1.8 (for COCO evaluation)
  
  

Installation

git clone https://github.com/OFA-Sys/OFA
pip install -r requirements.txt

Datasets and Checkpoints

See datasets.md and checkpoints.md.

Training & Inference

Below we provide methods for training and inference on different tasks. We provide both pretrained OFA-Large and OFA-Base in checkpoints.md. The scripts mentioned in this section are prepared for OFA-Large. For reproducing the downstreaming results of OFA-Base, we have also provided the corresponding finetuning and inference scripts for OFA-Base in the run_scripts/ folder.

We recommend that your workspace directory should be organized like this:

OFA/
├── checkpoints/
│   ├── ofa_base.pt
│   ├── ofa_large.pt
│   ├── caption_large_best_clean.pt
│   └── ...
├── criterions/
├── data/
├── dataset/
│   ├── caption_data/
│   ├── gigaword_data/
│   └── ...
├── fairseq/
├── models/
├── run_scripts/
├── tasks/
├── train.py
├── trainer.py
└── utils/

Image Processing

To ensure the efficiency of processing data, we did not store images with small files, but instead we encode them to base64 strings. Transforming image files to base64 strings is simple. Run the following code:

from PIL import Image
from io import BytesIO
import base64

img = Image.open(file_name) # path to file
img_buffer = BytesIO()
img.save(img_buffer, format=img.format)
byte_data = img_buffer.getvalue()
base64_str = base64.b64encode(byte_data) # bytes
base64_str = base64_str.decode("utf-8") # str

Pretraining

Below we provide methods for pretraining OFA.

1. Prepare the Dataset

To pretrain OFA, you should first download the dataset we provide (pretrain_data_examples.zip, a small subset of the original pretraining data). For your customed pretraining datasets, please prepare your training samples into the same format. pretrain_data_examples.zip contains 4 TSV files: vision_language_examples.tsv, text_examples.tsv, image_examples.tsv and detection_examples.tsv. Details of these files are as follows:

• vision_language_examples.tsv: Each line contains uniq-id, image (base64 string), caption, question, answer, ground-truth objects (objects appearing in the caption or question), dataset name (source of the data) and task type (caption, qa or visual gronunding). Prepared for the pretraining tasks of visual grounding, grounded captioning, image-text matching, image captioning and visual question answering.
• text_examples.tsv: Each line contains uniq-id and text. Prepared for the pretraining task of text infilling.
• image_examples.tsv: Each line contains uniq-id, image (base64 string, should be resized to 256*256 resolution) and image-code (generate the sparse codes for the central part of image through VQ-GAN). Prepared for the pretraining task of image infilling.
• detection_examples.tsv: Each line contains uniq-id, image (base64 string) and bounding box annotations (contains the top-left and bottom-right coordinates of the bounding box, object_id and object_name, seperated by commas). Prepared for the pretraining task of detection.

In addition, the folder negative_sample in pretrain_data_examples.zip contains three files all_captions.txt, object.txt and type2ans.json. The data in these files are used as negative samples for the image-text matching (ITM) task.

2. Pretraining

By default, the pretraining script will attempt to restore the released pretrained checkpoints of OFA-Base or OFA-Large and perform continuous pretraining. Continuous pretraining is more recommended, which achieves much better results compared with pretraining from scratch. For continuous pretraining, please download the pretrained weights in advance (see checkpoints.md) and put them in the correct directory OFA/checkpoints/. If not, the pretraining will begin from scratch.

cd run_scripts/pretraining
bash pretrain_ofa_large.sh # Pretrain OFA-Large. For OFA-Base, use pretrain_ofa_base.sh

If the pretrained OFA checkpoint is restored successfully, you will see the following information in the log:

INFO: Loaded checkpoint ../../checkpoints/ofa_large.pt

Image Captioning

We provide procedures to reproduce our results of image captioning on our paper below.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. The dataset zipfile caption_data.zip contains caption_stage1_train.tsv, caption_stage2_train.tsv, caption_val.tsv and caption_test.tsv. Each image corresponds to only 1 caption in caption_stage1_train.tsv and corresponds to multiple captions in other TSV files (about 5 captions per image). Each line of the dataset represents a caption sample with the following format. The information of uniq-id, image-id, caption, predicted object labels (taken from VinVL, not used), image base64 string are separated by tabs.

162365  12455   the sun sets over the trees beyond some docks.  sky&&water&&dock&&pole  /9j/4AAQSkZJ....UCP/2Q==

2. Finetuning

Following previous standard practice, we divide the finetuning process of image captioning into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 NVIDIA-V100 GPUs with 32GB memory (expected to obtain ~139.5 CIDEr on the validation set at this stage). In stage 2, we select the best checkpoint of stage 1 and train with CIDEr optimization on 8 NVIDIA-V100 GPUs. Note that CIDEr optimization is very unstable and requires careful hyperparameter tuning. If you encounter training errors in the stage2 finetuning, you can increase the batch size or reduce the learning rate. If neither of these works, you can directly set --freeze-resnet to freeze the inner states of batch normalization.

cd run_scripts/caption
nohup sh train_caption_stage1.sh > train_stage1.out &  # stage 1, train with cross-entropy loss
nohup sh train_caption_stage2.sh > train_stage2.out &  # stage 2, load the best ckpt of stage1 and train with CIDEr optimization 

3. Inference

Run the following commands to get your results and evaluate your model.

cd run_scripts/caption ; sh evaluate_caption.sh  # inference & evaluate

Text-to-Image Generation

This part provides procedures for the finetuning and inference of text-to-image generation. See below.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. The dataset zipfile coco_image_gen.zip contains coco_vqgan_train.tsv, coco_vqgan_dev.tsv and coco_vqgan_full_test.tsv. Each line of the dataset represents a sample with the following format. The information of uniq-id, image-code (produced by vqgan, a list of integers separated by single-whitespaces), lowercased caption are separated by tabs.

1	6674 4336 4532 5334 3251 5461 3615 2469 ...4965 4190 1846	the people are posing for a group photo.

The checkpoint zipfile image_gen_large_best.zip contains image_gen_large_best.pt, vqgan/last.ckpt, vqgan/model.yaml and clip/Vit-B-16.pt.

2. Shuffle the Training Data

(Optional, but achieves better result): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance.

cd dataset/image_gen
ln coco_vqgan_train.tsv coco_vqgan_train_1.tsv
for idx in `seq 1 9`;do shuf coco_vqgan_train_${idx}.tsv > coco_vqgan_train_$[${idx}+1].tsv;done # each file is used for an epoch

3. Finetuning

Following previous practice, we divide the finetuning process of image generating into two stages. In stage 1, we finetune OFA with cross-entropy loss on 4 8-V100-32G-GPU servers (expected to obtain ~32.5+ CLIP Score on the validation set at this stage). In stage 2, we select the last checkpoint of stage 1 and train with CLIP Score optimization on 4 8-V100-32G-GPU servers (expected to obtain ~34.0+ CLIP Score on the validation set at this stage). During the validation, the generated image will be dumped into _GEN_IMAGE_PATH_.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_image_gen_stage1_distributed.sh 
cd run_scripts/image_gen
nohup sh train_image_gen_stage1_distributed.sh # stage 1, train with cross-entropy loss
nohup sh train_image_gen_stage2_distributed.sh # stage 2, load the last ckpt of stage1 and train with CLIP Score optimization 

4. Inference

Run the command below to generate your images.

cd run_scripts/image_gen ; sh evaluate_image_gen.sh  # inference & evaluate (FID, IS and CLIP Score)

Visual Question Answering

Here we provide the finetuning and inference codes to reproduce the VQAv2 result reported in our paper (test-std 80.02). We believe much improvement on accuracy can still be achieved based on this codebase :)

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. The dataset zipfile vqa_data.zip is around 100G and the decompressed data costs around 135G disk storage, which contains the training, validation and testing samples together with other necessary data resources. (Since vqa_data.zip is large in size, we have also provided chunked parts of the dataset files for more convenient and stable downloading. Please refer to issue #68.) Following common practice, VG-QA samples are also included in the training data. To adapt to the seq2seq paradigm of OFA, we transform original VQA training questions with multiple golden answers into multiple training samples. For the original VQA validation set, we keep around 10k samples for our validation and utilize the other samples for training. Each line of the dataset represents a VQA sample with the following format. The information of question-id, image-id, question, answer (with confidence), predicted object labels (taken from VinVL, slightly brings around +0.1 accuracy improvement), image base64 string are separated by tabs.

79459   79459   is this person wearing shorts?  0.6|!+no    house&&short&&...&&sky  /9j/4AAQS...tigZ/9k=

For fine-tuning on customed VQA-formulated tasks, please refer to issue #76, #105 and #73 for more information.

2. Shuffle the Training Data

(Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around +0.3 improvement on VQA accuracy.

cd dataset/vqa_data
ln vqa_train.tsv vqa_train_1.tsv
for idx in `seq 1 9`;do shuf vqa_train_${idx}.tsv > vqa_train_$[${idx}+1].tsv;done # each file is used for an epoch

3. Finetuning

In our experiments, the VQA finetuning is performed on 4 8-A100-GPU servers (with RDMA). Here provides the finetuning script train_vqa_distributed.sh, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments. The command should be run on each worker.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_vqa_distributed.sh 
cd run_scripts/vqa
bash train_vqa_distributed.sh 

In our experiments, the finetuning costs around 36 hours (for 12 epochs). After each epoch, an evaluation on validation set is performed. The best validation accuracy during finetuning will be around 80.8. The log is saved in ${log_dir}.

(Update on validation time-cost) As will be mentioned in the 4. Inference section, we prepare 2 types of inference: beam-search and all-candidate inference. By default, all-candidate inference is used for validation during fine-tuning, which achieves better accuracy but costs much time. Now we have added a new option in the training scripts called --val-inference-type to switch the validation inference type during fine-tuning. If you feel the validation takes too long, you can refer to PR #79 to activate beam-search validation, which significantly takes much less time, with around 0.5-0.6 validation score degradation compared with all-candidate validation.

4. Inference

We provide 2 types of inference, beam-search (much faster but gets sub-optimal accuracy) and all-candidate evaluation (slower but best accuracy).

For beam-search inference, use the script evaluate_vqa_beam.sh. Refer to the command below. The inference on test set costs around 16 GPU hours. After inference on test set, the result JSON file will be dumped in the ${result_path} defined in the shell script. You can submit the result test_predict.json to EvalAI. Using our released finetuned checkpoint, beam-search inference will get 80.15 validation accuracy, 79.36 test-dev accuracy and 79.48 test-std accuracy (around 0.6 lower than all-candidate evaluation).

cd run_scripts/vqa
bash evaluate_vqa_beam.sh val # specify 'val' or 'test'

For all-candidate evaluation, we recommend to use the distributed script evaluate_vqa_allcand_distributed.sh. Please refer to the guide in the script to set the distributed configs before running. The result JSON file will be dumped in the ${result_path} defined in the shell script of rank-0 server. All-candidate evaluation computes scores on all the candidate answers in the VQA dataset, which achieves 80.82 validation accuracy, 79.87 test-dev accuracy and 80.02 test-std accuracy, reproducing our reported results in the paper. However, the inference on test set costs around 1k GPU hours, which is much slower.

# run on each worker after the distributed configs have been correctly set following the guide in evaluate_vqa_allcand_distributed.sh
cd run_scripts/vqa
bash evaluate_vqa_allcand_distributed.sh val # specify 'val' or 'test'

Visual Grounding (Referring Expression Comprehension)

Here provides procedures for you to prepare data, train, and evaluate your model on visual grounding.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. We provide RefCOCO (split by UNC), RefCOCO+ (split by UNC) and RefCOCOg (split by UMD) datasets. See RefCOCO and Refer for more details. Note that in the original dataset, each region-coord (or bounding box) may corresponds to multiple descriptive texts. We split these texts into multiple samples so that the region-coord in each sample corresponds to only one text. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, text, region-coord (separated by commas), image base64 string are separated by tabs.

79_1    237367  A woman in a white blouse holding a glass of wine.  230.79,121.75,423.66,463.06 9j/4AAQ...1pAz/9k=

2. Finetuning

Unlike the original paper, we finetune OFA with a drop-path rate of 0.2, and found that training with this hyper-parameter achieves better results. We will update the reported results of the paper later.

cd run_scripts/refcoco
nohup sh train_refcoco.sh > train_refcoco.out &  # finetune for refcoco
nohup sh train_refcocoplus.sh > train_refcocoplus.out &  # finetune for refcoco+
nohup sh train_refcocog.sh > train_refcocog.out &  # finetune for refcocog

3. Inference

Run the following commands for the evaluation.

cd run_scripts/refcoco ; sh evaluate_refcoco.sh  # inference & evaluate for refcoco/refcoco+/refcocog

Visual Entailment

We provide steps for you to reproduce our results in visual entailment. See the details below.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. Each line of the processed dataset represents a sample with the following format. The information of uniq-id, image-id, image base64 string, hypothesis, caption (or text premise), label are separated by tabs.

252244149.jpg#1r1n  252244149   /9j/4AAQ...MD/2Q==   a man in pink and gold is chewing on a wooden toothpick.   a man in pink is chewing a toothpick on the subway.   neutral 

2. Finetuning

In our experiments, the SNLI-VE finetuning is performed on 8 NVIDIA-V100 GPUs with 32GB memory. In this task, we experimented with only a few sets of hyperparameters. We believe that proper hyperparameter tuning can lead to further accuracy improvement.

cd run_scripts/snli_ve
nohup sh train_snli_ve.sh > train_snli_ve.out &  # finetune for snli_ve

3. Inference

Run the following command to obtain the results.

cd run_scripts/snli_ve ; sh evaluate_snli_ve.sh dev  # specify 'dev' or 'test'

GLUE

Here we provide steps for you to finetune and evaluate our model on language understanding tasks. We demonstrate our practice for the GLUE benchmark.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. we provide 7 language understanding datasets from GLUE benchmark, including COLA, MNLI, MRPC, QNLI, QQP, RTE and SST2. More details about these datasets can be found in this link.

2. Finetuning

For each task, we have tried multiple sets of hyperparameters (including learning rate, batch size, training epochs). The results under different sets of hyperparameters can be found in ${log_dir}.

cd run_scripts/glue
nohup sh train_cola.sh > train_cola.out &  # finetune for cola
nohup sh train_mnli.sh > train_mnli.out &  # finetune for mnli
nohup sh train_mrpc.sh > train_mrpc.out &  # finetune for mrpc
nohup sh train_qnli.sh > train_qnli.out &  # finetune for qnli
nohup sh train_qqp.sh > train_qqp.out &  # finetune for qqp
nohup sh train_rte.sh > train_rte.out &  # finetune for rte
nohup sh train_sst2.sh > train_sst2.out &  # finetune for sst2

Image Classification on ImageNet-1K

We provide the finetuning and inference codes which reproduce 85.0 ImageNet-1K accuracy, slightly better than reported in our paper.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. Our provided data is derived from the original ImageNet-1K (ILSVRC2012 train & validation) dataset and shares the same data split with it. To formulate the classification task into seq2seq paradigm, we use the synset words provided by Caffe as the generation target for each image class. Each line of the processed dataset represents a sample with the following format. The information of image base64 string, classification label (1-indexed, conform to the order in synset_words.txt), synset words of the label are separated by tabs.

_9j_4AAQS...fzX__Z  769 rugby ball

2. Shuffle the Training Data

(Optional, but achieves better finetuning accuracy): If the disk storage is sufficient, we recommend to prepare the shuffled training data for each epoch in advance. In our experiments, we use shuffling which brings around +0.2 improvement on ImageNet-1K accuracy.

cd dataset/imagenet_1k_data
ln imagenet_1k_train.tsv imagenet_1k_train_1.tsv
for idx in `seq 1 9`;do shuf imagenet_1k_train_${idx}.tsv > imagenet_1k_train_$[${idx}+1].tsv;done # each file is used for an epoch one by one

3. Finetuning

In our experiments, the ImageNet-1K finetuning is performed on 2 8-A100-GPU servers (with RDMA). Here provides the finetuning script train_imagenet_distributed.sh, which supports multi-server distributed training (as well as single-server training). Please refer to the comments in the beginning of the script and set the configs correctly according to your distribution environment. If you have shuffled the training data in the previous step, please correctly specify the training data path following the guide in the script comments. The command should be run on each worker. For quick evaluation during finetuning, by default we sample 20% of the original validation split and report accuracy on this subset after each epoch. The accuracy on the validation subset is generally ±0.1 relative to accuracy on the whole validation split.

# run on each worker after the distributed and data configs have been correctly set following the guide in train_imagenet_distributed.sh
cd run_scripts/image_classify
bash train_imagenet_distributed.sh

In our experiments, the finetuning costs around 80 hours (for 32 epochs). The best accuracy on validation subset during finetuning will be around 85.0. The log is saved in ${log_dir}.

4. Inference

To get the validation accuracy on the whole ImageNet-1K validation set, run the following command. The evaluation costs around 10 GPU hours. The accuracy will be reported in the stdout (expected to be around 85.0).

cd run_scripts/image_classify ; sh evaluate_imagenet.sh  # inference & evaluate for imagenet-1k

Gigaword

We provide steps for you to reproduce our results in Gigaword. See the details below.

1. Prepare the Dataset & Checkpoints

Download data (see datasets.md) and models (see checkpoints.md) and put them in the correct directory. The original dataset is taken from UniLM and we organized the data into the tsv format. Each line of the processed dataset represents a sample with the following format. The information of source and target texts are separated by tabs.

factory orders for manufactured goods rose #.# percent in september...  us september factory orders up #.# percent

2. Finetuning

Run the following command to train the model.

cd run_scripts/gigaword
nohup sh train_gigaword.sh > train_gigaword.out &  # finetune for gigaword

3. Inference

Run the following command to obtain the results (~36.43 rougeL).

cd run_scripts/gigaword ; sh evaluate_gigaword.sh  # inference & evaluate for gigaword

Gallery

Below we provide examples of OFA in text-to-image generation and open-ended VQA. Also, we demonstrate its performance in unseen task (Grounded QA) as well as unseen domain (Visual Grounding on images from unseen domains).

Text-to-Image Generation

Open-Ended VQA

Grounded QA (unseen task)

Visual Grounding (unseen domain)

Related Codebase

• Fairseq
• taming-transformers
  
  

Getting Involved

Feel free to submit Github issues or pull requests. Welcome to contribute to our project!

To contact us, never hestitate to send an email to zheluo.wp@alibaba-inc.com or junyang.ljy@alibaba-inc.com!

Citation

Please cite our papers if you find them helpful :)

@article{wang2022ofa,
  author    = {Peng Wang and
               An Yang and
               Rui Men and
               Junyang Lin and
               Shuai Bai and
               Zhikang Li and
               Jianxin Ma and
               Chang Zhou and
               Jingren Zhou and
               Hongxia Yang},
  title     = {OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence
               Learning Framework},
  journal   = {CoRR},
  volume    = {abs/2202.03052},
  year      = {2022}
}

@article{ofa_ocr,
  author       = {Junyang Lin and
                  Xuancheng Ren and
                  Yichang Zhang and
                  Gao Liu and
                  Peng Wang and
                  An Yang and
                  Chang Zhou},
  title        = {Transferring General Multimodal Pretrained Models to Text Recognition},
  journal      = {CoRR},
  volume       = {abs/2212.09297},
  year         = {2022}
}

@article{ofa_prompt,
  author       = {Hao Yang and
                  Junyang Lin and
                  An Yang and
                  Peng Wang and
                  Chang Zhou and
                  Hongxia Yang},
  title        = {Prompt Tuning for Generative Multimodal Pretrained Models},
  journal      = {CoRR},
  volume       = {abs/2208.02532},
  year         = {2022}
}

@article{mmspeech,
  title={MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition},
  author={Zhou, Xiaohuan and Wang, Jiaming and Cui, Zeyu and Zhang, Shiliang and Yan, Zhijie and Zhou, Jingren and Zhou, Chang},
  journal={arXiv preprint arXiv:2212.00500},
  year={2022}
}